基于改进的白优化算法和跨模式特征融合的增强错误信息检测模型
Guangyu Mu1,2, Xiaoqing Ju1, Hongduo Yan3
1School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China.
Biomimetics (Basel, Switzerland)
|March 26, 2025
概括
本研究介绍了IBWO-CASC模型,用于检测社交媒体上的多式联络错误信息. 这种新的方法在复杂的场景中提高了检测准确度和稳定性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 社交媒体上的多模式错误信息构成了重大挑战.
- 现有的检测方法在特征表示和跨模态语义对齐方面扎.
研究的目的:
- 提出一种有效的多式联运虚假信息检测模型.
- 解决特征表示和跨模态语义对齐问题.
主要方法:
- 开发了IBWO-CASC模型,集成了改进的贝卢加优化算法 (IBWO) 与交叉模式注意力特征融合.
- 通过适应性搜索和批量并行策略增强IBWO.
- 员工监督对比学习以实现特征对齐,并将跨模式注意力促进与全球-本地交互学习相结合.
主要成果:
- 在自构建的多式联运虚假信息数据集上,实现了97.41%的检测准确率.
- 与六个基线模型相比,显示了4.09%的精度改善.
- 在复杂的多式联运场景中展示了增强的稳定性.
结论:
- 该IBWO-CASC模型有效地检测多式联运错误信息.
- 提出的方法显著提高了检测准确性和稳定性.
- 这项工作推动了多式联运虚假信息检测领域的发展.
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